Article
Excess Energy Availability Score Overview
Benjamin T. House, Andy J. Galpin, Dan Garner, Vince Kreipke, and Thomas R. Wood

What Is the Excess Energy Availability Score?

When someone consumes more calories than they require, chronic excess energy availability will result in increased body fat storage in two primary areas, called subcutaneous (directly under skin) and visceral (beneath muscle & in and around organs) [1]. Initially, most fat is deposited subcutaneously. This region can be thought of as an energy sink, and once the storage capacity within subcutaneous fat is surpassed (sometimes called the “personal fat threshold”), energy excess begins to spill over into the blood and affect other organ systems [2, 3]. For example, once the subcutaneous capacity becomes overwhelmed, the body begins to store more fat viscerally - both in and around the organs [4]. Visceral fat accumulation has a much larger negative impact on metabolic health than subcutaneous fat [5-8].

Keywords: Excess Energy Availability, Subcutaneous Fat, Metabolic Health

Associated Biomarkers

Female Biomarkers Male Biomarkers
Triglycerides Triglycerides
Insulin Insulin
C-Peptide C-Peptide
HbA1c HbA1c
GGT GGT
hsCRP hsCRP
Leptin Leptin
Uric Acid Uric Acid
NLR NLR
HDL HDL

Experienced Physiological Effects:

  • Increased fatigue
  • Problems concentrating
  • Lower overall mood

Physiology Deep Dive:

Chronic excess energy availability will result in increased triglycerides, low-density lipoprotein (LDL) cholesterol, and ApoB-containing lipoproteins [1, 2, 9, 10] and a decrease in high-density lipoproteins (HDL) [2, 10, 11]. Chronic over consumption of Calories will also result in increases in glucose, insulin, c-peptide, and hemoglobin A1c [1, 2, 9, 10, 12-17]. Leptin will increase [1, 28-33], and chronic inflammation and elevated oxidative stress can develop [9, 12, 18-26]. Liver fat can accumulate, causing a rise in liver enzymes (AST + ALT) [9, 12, 18-26]. Uric acid can also increase in a state of chronic energy overload [27-29]. The immune system can become dysregulated, resulting in an increased neutrophil-to-lymphocyte ratio (NLR) [18, 30]. All of these deleterious effects of long-term excess energy availability can reduce quality of life [31, 32] and increase the risk of all-cause mortality [33-35].

Constraint Zones:

Green:

A prolonged period of excess energy availability is unlikely. Metabolic health markers are reassuring, allowing for either ongoing maintenance of current levels of energy intake or moving into a caloric surplus phase if that aligns with the athlete’s goals.

Yellow:

An excess energy availability signal is beginning to appear, and metabolic health markers are starting to be negatively affected. Consider decreasing energy intake and/or increasing energy expenditure. A body composition measurement may be warranted. *Some degree of this signal can be required or expected for some positions in sports that involve building and maintaining higher amounts of body mass [36-39]. 

Red:

Metabolic dysfunction is evident. A history of prolonged excess energy availability is likely, resulting in likely downstream consequences on other organ systems and causing systemic inflammation. Decreasing energy intake and/or increasing energy expenditure is advisable. A body composition measurement may be warranted. *Some degree of this signal can be required or expected for some positions in sports that involve building and maintaining higher amounts of body mass [36-39].

References

  1. Bray, G.A. and C. Bouchard, The biology of human overfeeding: A systematic review. Obes Rev, 2020.
  2. Taylor, R., et al., Aetiology of Type 2 diabetes in people with a 'normal' body mass index: testing the personal fat threshold hypothesis. Clin Sci (Lond), 2023. 137(16): p. 1333–1346.
  3. Taylor, R. and R.R. Holman, Normal weight individuals who develop type 2 diabetes: the personal fat threshold. Clin Sci (Lond), 2015. 128(7): p. 405–10.
  4. Janssen, J., Overnutrition, Hyperinsulinemia and Ectopic Fat: It Is Time for A Paradigm Shift in the Management of Type 2 Diabetes. Int J Mol Sci, 2024. 25(10).
  5. Fox, C.S., et al., Abdominal visceral and subcutaneous adipose tissue compartments: association with metabolic risk factors in the Framingham Heart Study. Circulation, 2007. 116(1): p. 39–48.
  6. Chartrand, D.J., et al., Visceral adiposity and liver fat as mediators of the association between cardiorespiratory fitness and plasma glucose-insulin homeostasis. Am J Physiol Endocrinol Metab, 2020. 319(3): p. E548–E556.
  7. Karlsson, T., et al., Contribution of genetics to visceral adiposity and its relation to cardiovascular and metabolic disease. Nat Med, 2019. 25(9): p. 1390–1395.
  8. Huang, Y., et al., Associations of Visceral Adipose Tissue, Circulating Protein Biomarkers, and Risk of Cardiovascular Diseases: A Mendelian Randomization Analysis. Front Cell Dev Biol, 2022. 10: p. 840866.
  9. Telle-Hansen, V.H., et al., A comprehensive metabolic profiling of the metabolically healthy obesity phenotype. Lipids Health Dis, 2020. 19(1): p. 90.
  10. Hoddy, K.K., et al., Insulin resistance persists despite a metabolically healthy obesity phenotype. Obesity (Silver Spring), 2022. 30(1): p. 39–44.
  11. Bluher, M., Metabolically Healthy Obesity. Endocr Rev, 2020. 41(3).
  12. Barrea, L., et al., Metabolically Healthy Obesity (MHO) vs. Metabolically Unhealthy Obesity (MUO) Phenotypes in PCOS: Association with Endocrine-Metabolic Profile, Adherence to the Mediterranean Diet, and Body Composition. Nutrients, 2021. 13(11).
  13. Oppert, J.M., et al., Plasma glucose, insulin, and glucagon before and after long-term overfeeding in identical twins. Metabolism, 1995. 44(1): p. 96–105.
  14. Janssen, J., Hyperinsulinemia and Its Pivotal Role in Aging, Obesity, Type 2 Diabetes, Cardiovascular Disease and Cancer. Int J Mol Sci, 2021. 22(15).
  15. Vaidya, R.A., et al., Hyperinsulinemia: an early biomarker of metabolic dysfunction. Front Clin Diabetes Healthc, 2023. 4: p. 1159664.
  16. Thomas, D.D., et al., Hyperinsulinemia: An Early Indicator of Metabolic Dysfunction. J Endocr Soc, 2019. 3(9): p. 1727–1747.
  17. Trico, D., et al., Alterations in Adipose Tissue Distribution, Cell Morphology, and Function Mark Primary Insulin Hypersecretion in Youth With Obesity. Diabetes, 2024. 73(6): p. 941–952.
  18. Bahadir, A., et al., Is the neutrophil-to-lymphocyte ratio indicative of inflammatory state in patients with obesity and metabolic syndrome? Anatol J Cardiol, 2015. 15(10): p. 816–22.
  19. Coku, V. and X. Shkembi, Serum Gamma-glutamyltransferase and Obesity: is there a Link? Med Arch, 2018. 72(2): p. 112–115.
  20. Puukka, K., et al., Additive effects of moderate drinking and obesity on serum gamma-glutamyl transferase activity. Am J Clin Nutr, 2006. 83(6): p. 1351–4; quiz 1448–9.
  21. Jalili, V., et al., The association between obesity with serum levels of liver enzymes, alanine aminotransferase, aspartate aminotransferase, alkaline phosphatase and gamma-glutamyl transferase in adult women. Endocrinol Diabetes Metab, 2022. 5(6): p. e367.
  22. Su, Z., L. Efremov, and R. Mikolajczyk, Differences in the levels of inflammatory markers between metabolically healthy obese and other obesity phenotypes in adults: A systematic review and meta-analysis. Nutr Metab Cardiovasc Dis, 2024. 34(2): p. 251–269.
  23. Baek, S.U. and J.H. Yoon, Systemic Inflammation Across Metabolic Obesity Phenotypes: A Cross-Sectional Study of Korean Adults Using High-Sensitivity C-Reactive Protein as a Biomarker. Int J Mol Sci, 2024. 25(21).
  24. Mankowska-Cyl, A., et al., Gamma-glutamyltransferase activity as a surrogate biomarker of metabolic health status in young nondiabetic obese women. Biomark Med, 2017. 11(5): p. 449–457.
  25. Xie, J., et al., Association between Liver Enzymes with Metabolically Unhealthy Obese Phenotype. Lipids Health Dis, 2018. 17(1): p. 198.
  26. Somi, M., et al., Can Gamma-glutamyl Transferase Predict Unhealthy Metabolic Phenotypes Among Healthcare Workers in Azar Cohort Study? Hepatitis Monthly, 2022. 22.
  27. Li, F., et al., Serum Uric Acid Levels and Metabolic Indices in an Obese Population: A Cross-Sectional Study. Diabetes Metab Syndr Obes, 2021. 14: p. 627–635.
  28. Yu, J., et al., Asymptomatic Hyperuricemia and Metabolically Unhealthy Obesity: A Cross-Sectional Analysis in the Tianning Cohort. Diabetes Metab Syndr Obes, 2021. 14: p. 1367–1374.
  29. Feng, X., et al., The Association Between Hyperuricemia and Obesity Metabolic Phenotypes in Chinese General Population: A Retrospective Analysis. Front Nutr, 2022. 9: p. 773220.
  30. Marra, A., et al., The neutrophil to lymphocyte ratio (NLR) positively correlates with the presence and severity of metabolic syndrome in obese adults, but not in obese children/adolescents. BMC Endocr Disord, 2023. 23(1): p. 121.
  31. Stephenson, J., et al., The association between obesity and quality of life: a retrospective analysis of a large-scale population-based cohort study. BMC Public Health, 2021. 21(1): p. 1990.
  32. Yao, Z., et al., Adverse effects of obesity on overall health, quality of life, and related physical health metrics: A cross-sectional and longitudinal study from the All of Us Research Program. J Intern Med, 2025. 297(6): p. 657–671.
  33. Salehidoost, R., et al., Diabetes and all-cause mortality, a 18-year follow-up study. Sci Rep, 2020. 10(1): p. 3183.
  34. Global, B.M.I.M.C., et al., Body-mass index and all-cause mortality: individual-participant-data meta-analysis of 239 prospective studies in four continents. Lancet, 2016. 388(10046): p. 776–86.
  35. Chen, F., et al., Joint effect of BMI and metabolic status on mortality among adults: a population-based longitudinal study in United States. Sci Rep, 2024. 14(1): p. 2775.
  36. Borchers, J.R., et al., Metabolic syndrome and insulin resistance in Division 1 collegiate football players. Med Sci Sports Exerc, 2009. 41(12): p. 2105–10.
  37. Wilkerson, G.B., J.T. Bullard, and D.W. Bartal, Identification of cardiometabolic risk among collegiate football players. J Athl Train, 2010. 45(1): p. 67–74.
  38. Guo, J., et al., Prevalence of metabolic syndrome and its components among Chinese professional athletes of strength sports with different body weight categories. PLoS One, 2013. 8(11): p. e79758.
  39. Murata, H., et al., Characteristics of body composition and cardiometabolic risk of Japanese male heavyweight Judo athletes. J Physiol Anthropol, 2016. 35: p. 10.
  40. Lean, M.E.J., et al., Durability of a primary care-led weight-management intervention for remission of type 2 diabetes: 2-year results of the DiRECT open-label, cluster-randomised trial. Lancet Diabetes Endocrinol, 2019. 7(5): p. 344–355.
  41. Lean, M.E., et al., Primary care-led weight management for remission of type 2 diabetes (DiRECT): an open-label, cluster-randomised trial. Lancet, 2018. 391(10120): p. 541–551.
  42. Lean, M.E., et al., 5-year follow-up of the randomised Diabetes Remission Clinical Trial (DiRECT) of continued support for weight loss maintenance in the UK: an extension study. Lancet Diabetes Endocrinol, 2024. 12(4): p. 233–246.
  43. Raben, A., et al., The PREVIEW intervention study: Results from a 3-year randomized 2 x 2 factorial multinational trial investigating the role of protein, glycaemic index and physical activity for prevention of type 2 diabetes. Diabetes Obes Metab, 2021. 23(2): p. 324–337.
  44. Khalafi, M., et al., A comparison of the impact of exercise training with dietary intervention versus dietary intervention alone on insulin resistance and glucose regulation in individual with overweight or obesity: a systemic review and meta-analysis. Crit Rev Food Sci Nutr, 2023. 63(28): p. 9349–9363.
  45. Cruz, L.C.D., et al., Low-Intensity Resistance Exercise Reduces Hyperglycemia and Enhances Glucose Control Over a 24-Hour Period in Women With Type 2 Diabetes. J Strength Cond Res, 2019. 33(10): p. 2826–2835.
  46. Battista, F., et al., Effect of exercise on cardiometabolic health of adults with overweight or obesity: Focus on blood pressure, insulin resistance, and intrahepatic fat-A systematic review and meta-analysis. Obes Rev, 2021. 22 Suppl 4(Suppl 4): p. e13269.
  47. Silva, F.M., et al., The effects of combined exercise training on glucose metabolism and inflammatory markers in sedentary adults: a systematic review and meta-analysis. Sci Rep, 2024. 14(1): p. 1936.

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